A number of techniques have been developed to detect an edge, boundary or layer in image data. The process for locating these features in an image is sometimes referred to as segmentation or edge detection. Conventional image processing systems and methods for edge detection in ophthalmic applications are generally predicated on obtaining and processing a rectangular data set, i.e., a data set 600 comprising an orderly array of image data points having i rows and j columns as shown in FIG. 1. The data set may be composed of, for instance, pixels. However, in some ophthalmic imaging applications, such as “across the cut” incisions, the scanning is done along a conical surface, such as the conical surface 602 shown in FIG. 1. Applying a rectangular data set to a conical surface leads to complications and difficulties in implementing a raster scan and displaying the processed image.
Specifically, as shown in FIG. 1, a rectangular data set 600, when applied to a conic surface results in an irregular spacing of the data points in successive rows along the radial coordinate, r, in conical space. As such, data points at large radial values are spaced at greater distances than are data points at lower values of r. In pulsed laser imaging systems, this means that the scan speed and/or pulse repetition rate must be finely controlled on a line by line basis in order to ensure that the collected image data is rectangular. This results in complex and difficult raster scan over the area to be imaged. Further, an excessive number of data points may be collected at some portions of the area to be imaged for the required resolution of the image due to the requirement that the collected data be in a rectangular format. This can lead needlessly large and lengthy calculations. Further, the resulting image obtained from a rectangular image data set applied to a conical surface may also be distorted because the displayed image 604 is displayed on a graphical user interface is usually provided as a rectangular image in which spacing between data sets in all the rows is substantially the same.
Improved image processing systems and methods are therefore needed that provide improved detection of edges, boundaries and layers in the imaged object while decreasing computation size and time, provide for simpler and faster raster scanning along conical surfaces and increase the precision of the displayed image.